int main(){
int rep_ct = 10000;
gsl_rng *r = apop_rng_alloc(0);
apop_db_open("data-census.db");
gsl_vector *base_data = apop_query_to_vector("select in_per_capita from income where sumlevel+0.0 =40");
double RI = apop_query_to_float("select in_per_capita from income where sumlevel+0.0 =40 and geo_id2+0.0=44");
gsl_vector *boot_sample = gsl_vector_alloc(base_data->size);
gsl_vector *replications = gsl_vector_alloc(rep_ct);
for (int i=0; i< rep_ct; i++){
one_boot(base_data, r, boot_sample);
gsl_vector_set(replications, i, apop_mean(boot_sample));
}
double stderror = sqrt(apop_var(replications));
double mean = apop_mean(replications);
printf("mean: %g; standard error: %g; (RI-mean)/stderr: %g; p value: %g\n",
mean, stderror, (RI-mean)/stderror, 2*gsl_cdf_gaussian_Q(fabs(RI-mean), stderror));
}
apop_model* apop_t_estimate(apop_data *d, apop_model *m){
Apop_assert(d, "No data with which to count df. (the default estimation method)");
Get_vmsizes(d); //vsize, msize1, msize2, tsize
apop_model *out = apop_model_copy(*m);
double vmu = vsize ? apop_mean(d->vector) : 0;
double v_sum_sq = vsize ? apop_var(d->vector)*(vsize-1) : 0;
double m_sum_sq = 0;
double mmu = 0;
if (msize1) {
apop_matrix_mean_and_var(d->matrix, &mmu, &m_sum_sq);
m_sum_sq *= msize1*msize2-1;
}
apop_data_add_names(out->parameters, 'r', "mean", "standard deviation", "df");
apop_data_set(out->parameters, 0, -1, (vmu *vsize + mmu * msize1*msize2)/tsize);
apop_data_set(out->parameters, 1, -1, sqrt((v_sum_sq*vsize + m_sum_sq * msize1*msize2)/(tsize-1)));
apop_data_set(out->parameters, 2, -1, tsize-1);
apop_data_add_named_elmt(out->info, "log likelihood", out->log_likelihood(d, out));
return out;
}
double find_tstat(gsl_vector *in){ return apop_mean(in)/sqrt(apop_var(in));}
